Data Engineer

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Fertil Comunicacao e Marketing Ltda.

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2008

📱 Media

🤝 B2B

Media • B2B

Fertil Comunicacao e Marketing Ltda. is a Brazilian communication and marketing agency that appears to provide digital marketing, communications, and web presence services. The example text suggests they manage hosted websites and implement security/anti-abuse checks (CAPTCHA-style verification) for site access, indicating they may also handle website administration or hosting-related configurations for clients. Their core focus is on marketing and communications delivered to business clients.

📋 Description

• Design, build and evolve the company’s data platform architecture. • Develop and maintain ETL/ELT pipelines, including API extraction, transformations, incremental loads and CDC (Change Data Capture). • Model data for Data Warehouse, Data Lake and Data Marts, applying dimensional modeling (Star Schema, Snowflake Schema). • Implement batch and streaming processing with event-driven architecture and asynchronous processing. • Ensure database performance and reliability: relational modeling, indexes, partitioning, query optimization and replication. • Establish Data Quality, Data Lineage, Data Catalog and Data Governance practices. • Orchestrate pipelines and handle historical data with versioning and traceability. • Implement pipeline observability: logs, metrics, monitoring and alerts. • Provide reliable and near real-time data for dashboards, operational analytics and business intelligence. • Prepare the data foundation for future Machine Learning initiatives. • Automate processes and deployments using GitHub Actions and Docker. • Collaborate with product, engineering and business teams to define requirements and KPIs.

🎯 Requirements

• Languages: Python and advanced SQL. • Databases: PostgreSQL and MySQL; relational modeling, indexes, partitioning, query optimization, replication and performance tuning. • Data Modeling: Data Warehouse, Data Lake, Star Schema, Snowflake Schema, Data Mart, dimensional modeling, normalization and denormalization. • ETL / ELT: building pipelines, API extraction, data transformation, incremental load and CDC (Change Data Capture). • Data Processing: batch processing, streaming, asynchronous processing and Event-Driven Architecture. • AWS: S3, Lambda, SQS, RDS, CloudWatch and IAM. • Messaging: Amazon SQS. • Version control: Git and GitHub. • Observability: logs, metrics, pipeline monitoring and alerts. • Automation: GitHub Actions. • Containers: Docker. • Data Engineering: Data Quality, Data Lineage, Data Catalog, Data Governance, data versioning, pipeline orchestration and historical data handling.

🏖️ Benefits

• Flexible working hours • Career progression plan